Modern wireless communication systems transmit digital data (including digitized voice signals) across an air interface by modulating the data onto a radio frequency (RF) carrier. The RF signal is received and processed by a receiver, to recover the data. The received signal includes, in addition to the data, impairments such as interference and noise that must be quantified (or estimated) and removed. These impairments arise both from transmission across the air interface (e.g., multipath interference, interference from other signals, noise from the channel) and in the RF and analog receiver circuits that process the received signal. The RF and analog circuit impairment of interest (referred to as RF impairment hereafter) includes phase noise, carrier frequency offset, and particularly IQ imbalance.
To assist receivers in assessing channel conditions, to remove channel-induced interference, it is known to transmit known reference signals, also known as pilots. In Orthogonal Frequency Division Multiplex (OFDM) systems, two types of pilot structures are defined, as depicted in FIG. 1. A so-called block-type pilot arrangement comprises pilot tones inserted into every sub-carrier of an OFDM symbol within a specific period. Block-type pilots are thus frequency-continuous and time-spaced. Block type pilots are useful under a slow fading channel, and estimation of the channel can be based on, e.g., least squares (LS) or minimum mean squared error (MMSE) algorithms.
A so-called comb-type pilot signal comprises pilot tones uniformly inserted into certain sub-carriers of each OFDM symbol, the sub-carriers spaced apart from each other (in time and frequency). The comb-type pilot arrangement was introduced to satisfy the need for equalizing the significant changes even in one OFDM block. Interpolation (in both time and frequency) is required to estimate the channel conditions of data sub-carriers.
IQ imbalance is a gain and/or phase difference in the in-phase (I) and quadrature (Q) phase components of a received signal. IQ imbalance is frequency dependent, especially for wide bandwidth channels. Frequency-dependent IQ imbalance primarily originates from analog channel select filters in zero-IF receivers. The transfer function of such a filter is defined by a number of poles and zeros. The sensitivity to component value mismatch is most prominent for those poles and zeros with high Q-value.
RF impairment estimation and compensation have been investigated for WLAN, in particular, IEEE 802.11a. These solutions are often limited, as they depend on the characteristics of the wireless protocol. For example, the multipath fading channel of WLAN is assumed to be quite static within a frame; this assumption does not hold for many other systems. Additionally, a block-type pilot signal called a preamble is transmitted for the purpose of RF impairment estimation. If the multipath fading remains constant within a frame, it is possible to utilize previously estimated channel coefficients, thereby easing RF impairment estimation. Also, if the block-type pilot signal is available during data transmission, it is easier to meet the required estimation accuracy without relying on decision feedback estimation. Finally, most of the prior art only deals with frequency-independent IQ imbalance, and focuses on only a subset of impairment parameters, for example, IQ imbalance, carrier frequency offset and channel coefficients (excluding phase noise).
Frequency-dependent IQ imbalance estimation using digital baseband FIR filter is proposed in a paper by G. Xing, M. Shen and H. Liu, titled “Frequency offset and I/Q imbalance compensation for direct-conversion receivers,” published in the IEEE Trans. on Wireless Commun., vol. 4, vol. 673-680, March 2005, the disclosure of which is incorporated herein by reference in its entirety. However, this solution is targeted for WLAN, and has a limited applicability to other systems—such as LTE, LTE-Advanced, and the like—where the channel varies rapidly with time, and RF impairment estimation relies on comb-type pilot signals during data transmission. More importantly, the proposed solution by Xing, et al. doesn't take into account phase noise. If phase noise is considered together with frequency-dependent IQ imbalance, the resulting signal model is considerably more complex than that presented by Xing, et al.